@inproceedings{1451afc29afb4cdabd21e91cb4056c85,
title = "Improving search effectiveness with field-based relevance modeling",
abstract = "Fields are a valuable auxiliary source of information in semi-structured HTML web documents. So, it is no surprise that ranking models have been designed to leverage this information to improve search effectiveness. We present the first (initial) study of utilizing field-based information in the relevance modeling framework. Fields play two different, and integrated, roles in our models: sources of information for inducing relevance models and units on which relevance models are applied for ranking. Our preliminary results suggest that field-based relevance modeling can improve precision at top ranks; specifically, to a greater extent than the commonly used BM25F and SDM-Fields field-based models. Further analysis shows that using field-based relevance models mainly improves the effectiveness of tail queries. Our findings suggest that using field-based information together with relevance modeling is a promising area of future exploration.",
keywords = "Field-based retrieval models, Relevance modeling, Web search",
author = "Binsheng Liu and Oren Kurland and Xiaolu Lu and Culpepper, {J. Shane}",
note = "Publisher Copyright: {\textcopyright} 2018 Copyright held by the owner/author(s).; 23rd Australasian Document Computing Symposium, ADCS 2018 ; Conference date: 11-12-2018 Through 12-12-2018",
year = "2018",
month = dec,
day = "11",
doi = "10.1145/3291992.3292005",
language = "אנגלית",
series = "ACM International Conference Proceeding Series",
editor = "Andrew Trotman and Bevan Koopman and Paul Thomas",
booktitle = "Proceedings of the 23rd Australasian Document Computing Symposium, ADCS 2018",
}